Towards Private, Robust, and Verifiable Crowdsensing Systems via Public Blockchains

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1893-1907
Number of pages15
Journal / PublicationIEEE Transactions on Dependable and Secure Computing
Volume18
Issue number4
Online published16 Sep 2019
Publication statusPublished - Jul 2021

Abstract

Public blockchains have emerged as a promising direction in revolutionizing existing data-driven systems relying on centralized service providers. Among others, one kind of such systems is the popular crowdsensing systems which promise convenient data collection and aggregation. Although promising, leveraging public blockchains to build crowdsensing systems is non-trivial and has to overcome several barriers. Firstly, public blockchains are transparent and lack support for data privacy. Secondly, participants from the open blockchain environment may misbehave in serving crowdsensing applications, like providing invalid data or doing aggregation incorrectly. Further, on-chain processing incurs monetary cost, so simply putting all workload on-chain is highly uneconomical and a delicate joint on-chain and off-chain design is required. In this paper, we take the first research attempt and explore a new design point to bridge public blockchains with crowdsensing systems. We propose a framework for building private, robust, and verifiable blockchain-empowered crowdsensing systems. It features an open service paradigm where blockchain nodes can rent out their computing resources to serve crowdsensing applications, with custom and full-fledged mechanisms to foster a healthy and economical ecosystem and to simultaneously tackle the challenges of data privacy, robustness against misbehaving participants, and service correctness assurance. Extensive experiments demonstrate our design's practicality.

Research Area(s)

  • Blockchain, Buildings, Cryptography, Data privacy, Ecosystems, Encrypted blockchain applications, secure crowdsensing systems, smart contracts, Task analysis